Why PCA first? Raw face images are high-dimensional (100×100 = 10,000 features). Training a neural network on 10,000 inputs per sample is slow and prone to overfitting. PCA compresses the data to ≈80 ...
A complete face recognition system that combines Principal Component Analysis (PCA) for dimensionality reduction and feature extraction (Eigenfaces) with an Artificial Neural Network (ANN) for ...
Abstract: Eigenspace-based face recognition corresponds to one of the most successful methodologies for the computational recognition of faces in digital images. Starting with the Eigenface-Algorithm, ...
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